Overview

Brought to you by YData

Dataset statistics

Number of variables11
Number of observations585058
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory53.6 MiB
Average record size in memory96.0 B

Variable types

Numeric7
Categorical2
Text2

Alerts

CRS_ARR_TIME is highly overall correlated with CRS_DEP_TIMEHigh correlation
CRS_DEP_TIME is highly overall correlated with CRS_ARR_TIMEHigh correlation
CRS_ELAPSED_TIME is highly overall correlated with DISTANCEHigh correlation
DISTANCE is highly overall correlated with CRS_ELAPSED_TIMEHigh correlation
ARR_DELAY has 10416 (1.8%) zerosZeros

Reproduction

Analysis started2024-07-26 20:45:57.464575
Analysis finished2024-07-26 20:46:07.170174
Duration9.71 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

DAY_OF_MONTH
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.223844
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 MiB
2024-07-26T16:46:07.212911image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q19
median16
Q324
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8801536
Coefficient of variation (CV)0.54735199
Kurtosis-1.1852382
Mean16.223844
Median Absolute Deviation (MAD)8
Skewness-0.025358995
Sum9491890
Variance78.857128
MonotonicityNot monotonic
2024-07-26T16:46:07.266491image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
24 20220
 
3.5%
13 20193
 
3.5%
31 20033
 
3.4%
17 20027
 
3.4%
21 19995
 
3.4%
20 19945
 
3.4%
10 19758
 
3.4%
28 19694
 
3.4%
27 19684
 
3.4%
23 19556
 
3.3%
Other values (21) 385953
66.0%
ValueCountFrequency (%)
1 17503
3.0%
2 17986
3.1%
3 17561
3.0%
4 16169
2.8%
5 18779
3.2%
6 19347
3.3%
7 18974
3.2%
8 17749
3.0%
9 17810
3.0%
10 19758
3.4%
ValueCountFrequency (%)
31 20033
3.4%
30 19476
3.3%
29 17331
3.0%
28 19694
3.4%
27 19684
3.4%
26 19287
3.3%
25 18414
3.1%
24 20220
3.5%
23 19556
3.3%
22 18089
3.1%

DAY_OF_WEEK
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0346496
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 MiB
2024-07-26T16:46:07.305490image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0724925
Coefficient of variation (CV)0.51367349
Kurtosis-1.3146608
Mean4.0346496
Median Absolute Deviation (MAD)2
Skewness-0.048214599
Sum2360504
Variance4.2952252
MonotonicityNot monotonic
2024-07-26T16:46:07.340388image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 97599
16.7%
7 93141
15.9%
6 88379
15.1%
4 79169
13.5%
5 77947
13.3%
3 76587
13.1%
2 72236
12.3%
ValueCountFrequency (%)
1 97599
16.7%
2 72236
12.3%
3 76587
13.1%
4 79169
13.5%
5 77947
13.3%
6 88379
15.1%
7 93141
15.9%
ValueCountFrequency (%)
7 93141
15.9%
6 88379
15.1%
5 77947
13.3%
4 79169
13.5%
3 76587
13.1%
2 72236
12.3%
1 97599
16.7%

AIRLINE
Categorical

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.9 MiB
Southwest Airlines Co.
124840 
Delta Air Lines Inc.
88066 
American Airlines Inc.
81377 
United Air Lines Inc.
62271 
SkyWest Airlines Inc.
56262 
Other values (10)
172242 

Length

Max length22
Median length21
Mean length19.827665
Min length9

Characters and Unicode

Total characters11600334
Distinct characters38
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEndeavor Air Inc.
2nd rowEndeavor Air Inc.
3rd rowEndeavor Air Inc.
4th rowEndeavor Air Inc.
5th rowEndeavor Air Inc.

Common Values

ValueCountFrequency (%)
Southwest Airlines Co. 124840
21.3%
Delta Air Lines Inc. 88066
15.1%
American Airlines Inc. 81377
13.9%
United Air Lines Inc. 62271
10.6%
SkyWest Airlines Inc. 56262
9.6%
Republic Airline 22650
 
3.9%
Alaska Airlines Inc. 22571
 
3.9%
JetBlue Airways 21108
 
3.6%
Spirit Air Lines 20650
 
3.5%
Envoy Air 19348
 
3.3%
Other values (5) 65915
11.3%

Length

2024-07-26T16:46:07.385930image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
inc 364437
19.9%
airlines 322483
17.6%
air 218817
12.0%
lines 170987
9.3%
southwest 124840
 
6.8%
co 124840
 
6.8%
delta 88066
 
4.8%
american 81377
 
4.4%
united 62271
 
3.4%
skywest 56262
 
3.1%
Other values (12) 216000
11.8%

Most occurring characters

ValueCountFrequency (%)
i 1348947
11.6%
1245322
 
10.7%
n 1093159
 
9.4%
e 1036386
 
8.9%
r 731746
 
6.3%
s 718251
 
6.2%
A 717340
 
6.2%
t 524164
 
4.5%
l 523578
 
4.5%
. 489277
 
4.2%
Other values (28) 3172164
27.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7925367
68.3%
Uppercase Letter 1940368
 
16.7%
Space Separator 1245322
 
10.7%
Other Punctuation 489277
 
4.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 1348947
17.0%
n 1093159
13.8%
e 1036386
13.1%
r 731746
9.2%
s 718251
9.1%
t 524164
 
6.6%
l 523578
 
6.6%
c 468464
 
5.9%
o 299587
 
3.8%
a 285241
 
3.6%
Other values (11) 895844
11.3%
Uppercase Letter
ValueCountFrequency (%)
A 717340
37.0%
I 364437
18.8%
S 218061
 
11.2%
L 170987
 
8.8%
C 124840
 
6.4%
D 88066
 
4.5%
U 62271
 
3.2%
W 56262
 
2.9%
E 35805
 
1.8%
R 22650
 
1.2%
Other values (5) 79649
 
4.1%
Space Separator
ValueCountFrequency (%)
1245322
100.0%
Other Punctuation
ValueCountFrequency (%)
. 489277
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9865735
85.0%
Common 1734599
 
15.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 1348947
13.7%
n 1093159
11.1%
e 1036386
10.5%
r 731746
 
7.4%
s 718251
 
7.3%
A 717340
 
7.3%
t 524164
 
5.3%
l 523578
 
5.3%
c 468464
 
4.7%
I 364437
 
3.7%
Other values (26) 2339263
23.7%
Common
ValueCountFrequency (%)
1245322
71.8%
. 489277
 
28.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11600334
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 1348947
11.6%
1245322
 
10.7%
n 1093159
 
9.4%
e 1036386
 
8.9%
r 731746
 
6.3%
s 718251
 
6.2%
A 717340
 
6.2%
t 524164
 
4.5%
l 523578
 
4.5%
. 489277
 
4.2%
Other values (28) 3172164
27.3%

ORIGIN
Text

Distinct336
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size8.9 MiB
2024-07-26T16:46:07.553208image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1755174
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAUS
2nd rowCVG
3rd rowIND
4th rowJFK
5th rowDTW
ValueCountFrequency (%)
atl 29406
 
5.0%
dfw 25688
 
4.4%
den 24914
 
4.3%
ord 21995
 
3.8%
lax 17253
 
2.9%
clt 16565
 
2.8%
las 15561
 
2.7%
sea 15520
 
2.7%
phx 13876
 
2.4%
mco 13315
 
2.3%
Other values (326) 390965
66.8%
2024-07-26T16:46:07.723503image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 200976
 
11.5%
L 160262
 
9.1%
S 151410
 
8.6%
D 139059
 
7.9%
T 93498
 
5.3%
O 89468
 
5.1%
C 89116
 
5.1%
M 79765
 
4.5%
F 73200
 
4.2%
N 68866
 
3.9%
Other values (16) 609554
34.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1755174
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 200976
 
11.5%
L 160262
 
9.1%
S 151410
 
8.6%
D 139059
 
7.9%
T 93498
 
5.3%
O 89468
 
5.1%
C 89116
 
5.1%
M 79765
 
4.5%
F 73200
 
4.2%
N 68866
 
3.9%
Other values (16) 609554
34.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 1755174
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 200976
 
11.5%
L 160262
 
9.1%
S 151410
 
8.6%
D 139059
 
7.9%
T 93498
 
5.3%
O 89468
 
5.1%
C 89116
 
5.1%
M 79765
 
4.5%
F 73200
 
4.2%
N 68866
 
3.9%
Other values (16) 609554
34.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1755174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 200976
 
11.5%
L 160262
 
9.1%
S 151410
 
8.6%
D 139059
 
7.9%
T 93498
 
5.3%
O 89468
 
5.1%
C 89116
 
5.1%
M 79765
 
4.5%
F 73200
 
4.2%
N 68866
 
3.9%
Other values (16) 609554
34.7%

DEST
Text

Distinct336
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size8.9 MiB
2024-07-26T16:46:07.854293image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1755174
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRDU
2nd rowAUS
3rd rowJFK
4th rowDTW
5th rowMBS
ValueCountFrequency (%)
atl 29363
 
5.0%
dfw 25729
 
4.4%
den 24773
 
4.2%
ord 21878
 
3.7%
lax 17289
 
3.0%
clt 16560
 
2.8%
las 15643
 
2.7%
sea 15542
 
2.7%
phx 13879
 
2.4%
mco 13229
 
2.3%
Other values (326) 391173
66.9%
2024-07-26T16:46:08.027130image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 201174
 
11.5%
L 160358
 
9.1%
S 151733
 
8.6%
D 138834
 
7.9%
T 93582
 
5.3%
O 89270
 
5.1%
C 89042
 
5.1%
M 79663
 
4.5%
F 73182
 
4.2%
N 68847
 
3.9%
Other values (16) 609489
34.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1755174
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 201174
 
11.5%
L 160358
 
9.1%
S 151733
 
8.6%
D 138834
 
7.9%
T 93582
 
5.3%
O 89270
 
5.1%
C 89042
 
5.1%
M 79663
 
4.5%
F 73182
 
4.2%
N 68847
 
3.9%
Other values (16) 609489
34.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 1755174
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 201174
 
11.5%
L 160358
 
9.1%
S 151733
 
8.6%
D 138834
 
7.9%
T 93582
 
5.3%
O 89270
 
5.1%
C 89042
 
5.1%
M 79663
 
4.5%
F 73182
 
4.2%
N 68847
 
3.9%
Other values (16) 609489
34.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1755174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 201174
 
11.5%
L 160358
 
9.1%
S 151733
 
8.6%
D 138834
 
7.9%
T 93582
 
5.3%
O 89270
 
5.1%
C 89042
 
5.1%
M 79663
 
4.5%
F 73182
 
4.2%
N 68847
 
3.9%
Other values (16) 609489
34.7%

CRS_DEP_TIME
Real number (ℝ)

HIGH CORRELATION 

Distinct1230
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1333.6551
Minimum1
Maximum2359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 MiB
2024-07-26T16:46:08.125129image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile600
Q1905
median1322
Q31747
95-th percentile2145
Maximum2359
Range2358
Interquartile range (IQR)842

Descriptive statistics

Standard deviation503.71329
Coefficient of variation (CV)0.37769381
Kurtosis-1.0778364
Mean1333.6551
Median Absolute Deviation (MAD)422
Skewness0.093413594
Sum7.8026561 × 108
Variance253727.08
MonotonicityNot monotonic
2024-07-26T16:46:08.176089image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 11962
 
2.0%
700 8604
 
1.5%
800 5149
 
0.9%
630 3702
 
0.6%
615 3556
 
0.6%
900 3549
 
0.6%
1000 3353
 
0.6%
730 3279
 
0.6%
830 3019
 
0.5%
715 2847
 
0.5%
Other values (1220) 536038
91.6%
ValueCountFrequency (%)
1 14
 
< 0.1%
4 1
 
< 0.1%
5 2
 
< 0.1%
8 27
< 0.1%
9 2
 
< 0.1%
10 38
< 0.1%
11 4
 
< 0.1%
12 4
 
< 0.1%
14 7
 
< 0.1%
15 65
< 0.1%
ValueCountFrequency (%)
2359 999
0.2%
2358 67
 
< 0.1%
2357 117
 
< 0.1%
2356 57
 
< 0.1%
2355 227
 
< 0.1%
2354 57
 
< 0.1%
2353 82
 
< 0.1%
2352 8
 
< 0.1%
2351 4
 
< 0.1%
2350 135
 
< 0.1%

CRS_ARR_TIME
Real number (ℝ)

HIGH CORRELATION 

Distinct1305
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1471.9446
Minimum1
Maximum2359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 MiB
2024-07-26T16:46:08.226471image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile657
Q11045
median1503
Q31922
95-th percentile2300
Maximum2359
Range2358
Interquartile range (IQR)877

Descriptive statistics

Standard deviation537.72241
Coefficient of variation (CV)0.36531429
Kurtosis-0.47719119
Mean1471.9446
Median Absolute Deviation (MAD)432
Skewness-0.303095
Sum8.6117299 × 108
Variance289145.39
MonotonicityNot monotonic
2024-07-26T16:46:08.277285image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2359 2973
 
0.5%
1900 1834
 
0.3%
2100 1795
 
0.3%
1810 1736
 
0.3%
1915 1721
 
0.3%
1855 1711
 
0.3%
1140 1695
 
0.3%
2145 1691
 
0.3%
905 1648
 
0.3%
2000 1639
 
0.3%
Other values (1295) 566615
96.8%
ValueCountFrequency (%)
1 75
 
< 0.1%
2 152
 
< 0.1%
3 274
 
< 0.1%
4 149
 
< 0.1%
5 742
0.1%
6 74
 
< 0.1%
7 49
 
< 0.1%
8 74
 
< 0.1%
9 116
 
< 0.1%
10 581
0.1%
ValueCountFrequency (%)
2359 2973
0.5%
2358 797
 
0.1%
2357 687
 
0.1%
2356 534
 
0.1%
2355 1260
0.2%
2354 489
 
0.1%
2353 549
 
0.1%
2352 559
 
0.1%
2351 279
 
< 0.1%
2350 935
 
0.2%

CRS_ELAPSED_TIME
Real number (ℝ)

HIGH CORRELATION 

Distinct445
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean145.61639
Minimum23
Maximum671
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 MiB
2024-07-26T16:46:08.328742image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile64
Q191
median128
Q3175
95-th percentile310
Maximum671
Range648
Interquartile range (IQR)84

Descriptive statistics

Standard deviation73.367314
Coefficient of variation (CV)0.50383966
Kurtosis2.0907867
Mean145.61639
Median Absolute Deviation (MAD)40
Skewness1.3606067
Sum85194035
Variance5382.7628
MonotonicityNot monotonic
2024-07-26T16:46:08.383325image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85 11728
 
2.0%
90 11646
 
2.0%
80 10073
 
1.7%
70 9312
 
1.6%
110 8755
 
1.5%
115 8737
 
1.5%
75 8590
 
1.5%
95 7877
 
1.3%
105 7413
 
1.3%
135 7377
 
1.3%
Other values (435) 493550
84.4%
ValueCountFrequency (%)
23 31
 
< 0.1%
26 30
 
< 0.1%
33 31
 
< 0.1%
34 62
 
< 0.1%
35 113
 
< 0.1%
36 241
 
< 0.1%
37 335
0.1%
38 357
0.1%
39 420
0.1%
40 837
0.1%
ValueCountFrequency (%)
671 7
 
< 0.1%
670 21
< 0.1%
666 16
 
< 0.1%
665 1
 
< 0.1%
655 31
< 0.1%
645 9
 
< 0.1%
615 5
 
< 0.1%
602 7
 
< 0.1%
600 15
 
< 0.1%
585 52
< 0.1%

DISTANCE
Real number (ℝ)

HIGH CORRELATION 

Distinct1476
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean846.28638
Minimum31
Maximum5095
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 MiB
2024-07-26T16:46:08.437066image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile177
Q1402
median683
Q31076
95-th percentile2253
Maximum5095
Range5064
Interquartile range (IQR)674

Descriptive statistics

Standard deviation610.84477
Coefficient of variation (CV)0.72179441
Kurtosis2.3567987
Mean846.28638
Median Absolute Deviation (MAD)326
Skewness1.4233379
Sum4.9512662 × 108
Variance373131.34
MonotonicityNot monotonic
2024-07-26T16:46:08.491234image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
337 3273
 
0.6%
399 2551
 
0.4%
594 2478
 
0.4%
296 2383
 
0.4%
404 2354
 
0.4%
447 2238
 
0.4%
862 2231
 
0.4%
100 2195
 
0.4%
328 2157
 
0.4%
867 2136
 
0.4%
Other values (1466) 561062
95.9%
ValueCountFrequency (%)
31 61
 
< 0.1%
41 62
 
< 0.1%
61 51
 
< 0.1%
67 385
0.1%
68 90
 
< 0.1%
69 21
 
< 0.1%
70 57
 
< 0.1%
73 741
0.1%
74 175
 
< 0.1%
75 415
0.1%
ValueCountFrequency (%)
5095 44
< 0.1%
4983 106
< 0.1%
4962 18
 
< 0.1%
4817 10
 
< 0.1%
4502 62
< 0.1%
4475 44
< 0.1%
4243 62
< 0.1%
4213 10
 
< 0.1%
4184 44
< 0.1%
3972 62
< 0.1%

ARR_DELAY
Real number (ℝ)

ZEROS 

Distinct1358
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.641825
Minimum-86
Maximum3337
Zeros10416
Zeros (%)1.8%
Negative312657
Negative (%)53.4%
Memory size8.9 MiB
2024-07-26T16:46:08.542386image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum-86
5-th percentile-24
Q1-13
median-2
Q320
95-th percentile116
Maximum3337
Range3423
Interquartile range (IQR)33

Descriptive statistics

Standard deviation72.101824
Coefficient of variation (CV)4.3325671
Kurtosis152.87673
Mean16.641825
Median Absolute Deviation (MAD)13
Skewness9.0734374
Sum9736433
Variance5198.673
MonotonicityNot monotonic
2024-07-26T16:46:08.600783image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-10 15439
 
2.6%
-11 15407
 
2.6%
-9 15156
 
2.6%
-12 15027
 
2.6%
-8 14946
 
2.6%
-7 14693
 
2.5%
-13 14628
 
2.5%
-6 14129
 
2.4%
-14 14046
 
2.4%
-15 13315
 
2.3%
Other values (1348) 438272
74.9%
ValueCountFrequency (%)
-86 1
 
< 0.1%
-85 1
 
< 0.1%
-74 1
 
< 0.1%
-73 2
< 0.1%
-72 1
 
< 0.1%
-71 1
 
< 0.1%
-69 1
 
< 0.1%
-68 2
< 0.1%
-66 3
< 0.1%
-65 2
< 0.1%
ValueCountFrequency (%)
3337 1
< 0.1%
2980 1
< 0.1%
2912 1
< 0.1%
2891 1
< 0.1%
2854 1
< 0.1%
2786 1
< 0.1%
2748 1
< 0.1%
2429 1
< 0.1%
2424 1
< 0.1%
2418 1
< 0.1%

ARR_DEL15
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.9 MiB
0.0
417978 
1.0
167080 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1755174
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 417978
71.4%
1.0 167080
 
28.6%

Length

2024-07-26T16:46:08.654494image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-26T16:46:08.863657image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 417978
71.4%
1.0 167080
 
28.6%

Most occurring characters

ValueCountFrequency (%)
0 1003036
57.1%
. 585058
33.3%
1 167080
 
9.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1170116
66.7%
Other Punctuation 585058
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1003036
85.7%
1 167080
 
14.3%
Other Punctuation
ValueCountFrequency (%)
. 585058
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1755174
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1003036
57.1%
. 585058
33.3%
1 167080
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1755174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1003036
57.1%
. 585058
33.3%
1 167080
 
9.5%

Interactions

2024-07-26T16:46:05.869432image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:02.625439image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:03.256818image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:03.747731image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:04.246248image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:04.736755image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:05.327593image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:05.938730image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:02.722363image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:03.325032image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:03.821251image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:04.315161image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:04.809483image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:05.412199image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:06.007076image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:02.917886image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:03.392581image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:03.892938image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:04.382709image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:04.881171image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:05.493002image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:06.074224image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:02.985276image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:03.460302image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:03.961385image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:04.444837image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:04.953672image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:05.567247image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:06.140958image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:03.052484image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:03.528949image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:04.030681image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:04.515698image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:05.055838image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:05.641699image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:06.213497image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:03.121617image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:03.604155image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:04.104634image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:04.589623image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:05.148349image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:05.731168image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:06.279434image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:03.189634image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:03.671890image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:04.176461image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:04.664131image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:05.237011image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T16:46:05.800379image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Correlations

2024-07-26T16:46:08.892077image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
AIRLINEARR_DEL15ARR_DELAYCRS_ARR_TIMECRS_DEP_TIMECRS_ELAPSED_TIMEDAY_OF_MONTHDAY_OF_WEEKDISTANCE
AIRLINE1.0000.1550.0250.0600.0500.1750.0150.0270.173
ARR_DEL150.1551.0000.1640.2560.2530.0690.0800.0780.066
ARR_DELAY0.0250.1641.0000.2230.2550.0240.0320.0640.039
CRS_ARR_TIME0.0600.2560.2231.0000.7330.0360.0030.0040.033
CRS_DEP_TIME0.0500.2530.2550.7331.000-0.0120.0050.002-0.010
CRS_ELAPSED_TIME0.1750.0690.0240.036-0.0121.000-0.0040.0160.984
DAY_OF_MONTH0.0150.0800.0320.0030.005-0.0041.000-0.014-0.005
DAY_OF_WEEK0.0270.0780.0640.0040.0020.016-0.0141.0000.018
DISTANCE0.1730.0660.0390.033-0.0100.984-0.0050.0181.000

Missing values

2024-07-26T16:46:06.371622image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-26T16:46:06.676393image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DAY_OF_MONTHDAY_OF_WEEKAIRLINEORIGINDESTCRS_DEP_TIMECRS_ARR_TIMECRS_ELAPSED_TIMEDISTANCEARR_DELAYARR_DEL15
016Endeavor Air Inc.AUSRDU10071412185.01162.0-8.00.0
116Endeavor Air Inc.CVGAUS745922157.0958.03.00.0
216Endeavor Air Inc.INDJFK710928138.0665.024.01.0
316Endeavor Air Inc.JFKDTW16451900135.0509.0-7.00.0
416Endeavor Air Inc.DTWMBS2115221055.098.0-2.00.0
516Endeavor Air Inc.LGAMCI16151836201.01107.0-5.00.0
616Endeavor Air Inc.ILMLGA13351525110.0500.0-17.00.0
716Endeavor Air Inc.LGAILM9151116121.0500.0-20.00.0
816Endeavor Air Inc.PVDLGA70081070.0143.0-21.00.0
916Endeavor Air Inc.RDUDCA60071777.0227.013.00.0
DAY_OF_MONTHDAY_OF_WEEKAIRLINEORIGINDESTCRS_DEP_TIMECRS_ARR_TIMECRS_ELAPSED_TIMEDISTANCEARR_DELAYARR_DEL15
601856311Republic AirlineEWRBOS845100883.0200.0-17.00.0
601857311Republic AirlineDCALEX2140231696.0414.0-24.00.0
601858311Republic AirlineLGADCA1320145595.0214.0-22.00.0
601859311Republic AirlineBOSLGA1100122888.0184.0-9.00.0
601860311Republic AirlineINDIAD1941212099.0476.027.01.0
601861311Republic AirlineIADIND17051853108.0476.055.01.0
601862311Republic AirlineIADSAV9001059119.0515.0-26.00.0
601863311Republic AirlinePWMIAD530715105.0493.016.01.0
601864311Republic AirlineIADIND22072352105.0476.015.01.0
601865311Republic AirlineSAVIAD14001545105.0515.0-17.00.0